System and method for detecting child distress and generating suggestion for relieving child distress

ABSTRACT

The present invention provides a system and method for child monitoring. The child monitoring system comprises an audio sensor, a video sensor, an input module, a processing module, and a display module. The audio sensor is configured to capture speech data, the video sensor is configured to capture activity data. The input module is configured to receive one or more child parameters. The processing module is configured to generate a suggestion in response to the computation of the speech data, the activity data, and the one or more child parameters with a suggestion database or a predefined suggestion database. The display module is configured to display the suggestion and prompt a parent to provide an input. Further, the suggestion database is updated in response to the input provided by the parent. The processing module uses one or more machine learning modules to generate the suggestion.

FIELD OF THE INVENTION

The present invention provides a child monitoring system configured todetect child distress and provide suggestions to a parent, and inparticularly, to the system to update a suggestion database in responseto an input provided by the parent based on the suggestion outcome.

BACKGROUND OF THE INVENTION

Children often cry for several seasons, for example, hunger, tiredness,discomfort, wet or dirty nappies diapers, or just dreams. The followingare some cases of the child crying: the child is hungry or eats toomuch. The child needs to change clothes or change diapers. The childfeels too hot or too cold. The child wants to be carried. The childwants to sleep. In addition to the above reasons, there is a kind of“crying-and-cry” in the night, and the child may cry for any reason atnight, sometimes lasts for one or two months, has no body discomfort, isneither hungry, nor sleepy or simply wants to cry, and the child oftencauses headache for adults.

Common stressors in a child include dirty or wet diapers, pain, gas,hunger, boredom, fatigue, developmental crying (PURPLE crying), fever,reactive airway disease, and more. In particular, PURPLE crying isespecially troublesome for parents. Therefore, it may be important torecognize communication factors from child speech, gestures, and othercommunication cues to determine the emotional or physiological state ofthe child during an interaction.

In recent years, the use of computerized systems for analyzingcommunication cues has grown through the implementation of language andgesture interfaces. In the current arts, emotion recognizers operate byassigning category labels to emotional states, such as “angry” or “sad,”relying on signal processing and pattern recognition techniques.Emotional recognition may be performed by analyzing speech acousticsand/or facial expressions to target an emotional category orrepresentation. However, none of the prior art systems are able torecognize the emotional state of the child correctly.

Therefore, a child monitoring system is needed for helping parents orother caregivers to monitor child behavior and generate a suggestion tocure child distress.

SUMMARY OF THE INVENTION

Embodiment of the present invention provides a child monitoring systemconfigured to detect when a child is crying or in distress and provide asuggestion to a caretaker or parent of the child.

The present invention provides a child monitoring system comprises of atleast one audio sensor, at least one video sensor, an input module, aprocessing module, and a display module. The at least one audio sensoris located at a first location and is configured to capture a speechdata associated with a child. The at least one video sensor is locatedat a second location and is configured to capture an activity dataassociated with the child. The input module is configured to receive oneor more child parameters. The processing module comprises a speechrecognition module, an activity recognition module, and a classifiermodule. The speech recognition module is configured to receive thespeech data from the at least one audio sensor and determine a firstcategory information and a time associated with the first categoryinformation in response to the computation of the speech data associatedwith the child by using a speech recognition machine learning module.The activity recognition module is configured to receive the activitydata from the at least one video sensor and determine a second categoryinformation in response to the computation of the activity data by usingan activity recognition machine learning module. The classifier moduleis configured to receive the first category information and the timeassociated with the first category information from the speechrecognition module and receive the second category information from theactivity recognition module and one or more child parameters from theinput module. Further, the classifier module comprises a suggestionmachine learning module that is configured to generate a firstsuggestion in response to the presence of at least one or moresuggestions associated with the first category information, the timeassociated with the first category information, the second categoryinformation, and one or more child parameters in a suggestion database,or generate a second suggestion in response to the presence of at leastone or more suggestions associated with the first category information,the time associated with the first category information, the secondcategory information, and one or more child parameters in a predefinedchild development database. The display module is configured to displaythe first suggestion, or the second suggestion received from theclassifier module. The display module is configured to prompt the parentor caretaker to provide input in response to the suggestion displayed bythe display module. The input can be YES, the suggestion is helpful, NOthe suggestion is not helpful, or the parent can provide a suggestionthat enables the child to calm down. Further, the suggestion database isconfigured to be updated in response to receiving the input, generatedfrom the interaction with the first suggestion, or the second suggestiondisplayed on the display module, from the display module.

In one exemplary embodiment, the present invention provides a childmonitoring method. The method is configured for capturing a speech dataassociated with a child using an audio sensor, capturing an activitydata associated with the child using a video sensor, receiving one ormore child parameters, determining a first category information and atime associated with the first category information in response to thecomputation of the speech data, received by a speech recognition moduleof a processing module, using a speech recognition machine learningmodule, determining a second category information in response to thecomputation of the activity data, received by an activity recognitionmodule of the processing module, using an activity recognition machinelearning module. The method is further configured for receiving, by aclassifier module of the processing module, the first categoryinformation and the time associated with the first category informationfrom the speech recognition module, the second category information fromthe activity recognition module, and one or more child parameters. Theclassifier module comprises a suggestion machine learning moduleconfigured for generating a first suggestion in response to the presenceof at least one or more suggestions associated with the first categoryinformation, the time associated with the first category information,the second category information, and one or more child parameters in asuggestion database, or generating a second suggestion in response tothe presence of at least one or more suggestions associated with thefirst category information, the time associated with the first categoryinformation, the second category information, and one or more childparameters in a predefined child development database. Further, themethod is configured for displaying, by a display module, the firstsuggestion, or the second suggestion received from the classifier moduleand updating the suggestion database in response to receiving an input,generated from the interaction with the first suggestion, or the secondsuggestion displayed on the display module, from the display module.

In one exemplary embodiment, the speech data is at least one of anacoustic data, a lexical data, and a linguistic data. The first categoryinformation is at least one of a crying, sad, happy, angry, tired,frustrated, bored, delighted, extremely delighted, discomfort, positive,neutral, negative, very disappointed, hungry, frustrated, confused,overstimulation, development crying, thirst crying, teething crying, andsleepiness. The time can be morning, afternoon, evening, and night.

In yet another exemplary embodiment, the second category information isat least one of the but not limited to running, playing, eating,crawling, lying down, standing up, and sleeping. The one or more childparameters are age, gender, height, and weight.

In yet another exemplary embodiment, the predefined child developmentdatabase comprises suggestions related to a growing and developingchild. For example, the child may be crying due to teething problems,disturbed sleeping, etc.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention described herein are exemplary, andnot restrictive. Embodiments will now be described, by way of examples,with reference to the accompanying drawings. In these drawings, eachidentical or nearly identical component that is illustrated in variousfigures is represented by a reference number. For purposes of clarity,not every component is labeled in every drawing. The drawings are notnecessarily drawn to scale, with emphasis instead being placed onillustrating various aspects of the techniques and devices describedherein.

The foregoing and other objects, aspects, and advantages are betterunderstood from the following detailed description of a preferredembodiment of the invention with reference to the drawings, in which:

FIG. 1 illustrates a block diagram of the child monitoring system, inaccordance with a preferred embodiment of the present invention.

FIG. 2 illustrates an example flow chart of the child monitoring usingmachine learning, in accordance with an exemplary embodiment of thepresent invention.

FIG. 3 shows a user interface of the electronic device for providingsuggestions and receiving feedback, in accordance with an exemplaryembodiment of the present invention.

FIG. 4 illustrates a flow chart of a child monitoring method, inaccordance with an exemplary embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

Overview

With reference to the figures provided, embodiments of the presentinvention are now described in detail.

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the invention. It will be apparent, however, to oneskilled in the art that the invention can be practiced without thesespecific details. In other instances, structures, devices, activities,and methods are shown using schematics, use cases, and/or flow diagramsin order to avoid obscuring the invention. Although the followingdescription contains many specifics for the purposes of illustration,anyone skilled in the art will appreciate that many variations and oralterations to suggested details are within the scope of the presentinvention. Similarly, although many of the features of the presentinvention are described in terms of each other, or in conjunction witheach other, one skilled in the art will appreciate that many of thesefeatures can be provided independently of other features. Accordingly,this description of the invention is set forth without any loss ofgenerality to, and without imposing limitations upon, the invention.

Embodiment of the present invention provides a child monitoring systemconfigured to detect when a child is crying or in distress and provide asuggestion to a caretaker or parent of the child. Some embodimentsprovide a child monitoring method configured for detecting childdistress and generating a suggestion in response to the child distress.The child can be an infant, toddler, or 1-2-year-old baby who is unableto express himself/herself. In one embodiment the child can be a2-5-year-old child who can express themselves through non verbal cues orgestures only.

The child may be become fussy during sleep due to bad dreams, growingpain, digestive problems, stomach discomfort, needed a diaper change,tired, frustrated, bored, discomfort, very disappointed, hunger,confusion, overstimulation, development crying, thirst crying, andsleepiness.

FIG. 1 illustrates a block diagram of the child monitoring system (100),in accordance with a preferred embodiment of the present invention. Thechild monitoring system (100) comprising at least one audio sensor(102), at least one video sensor (104), an input module (106), aprocessing module (110), and a display module (112). The at least oneaudio sensor (102) is located at a first location and is configured tocapture a speech data associated with a child. Speech data may includebut are not limited to pitch, length, volume, tone, energy, variation,intensity, clustering, and other audio features commonly used for theclassification of audio information known in the arts.

The at least one video sensor (104) is located at a second location andis configured to capture an activity data associated with the child. Theinput module (106) is configured to receive one or more childparameters. The child parameters are at least one of age, gender,height, and weight of the child.

The at least one audio sensor (102), the at least one video sensor(104), and the input module (106) are configured to transmit the speechdata, the activity data, and the one or more child parameters to theprocessing module (110) through a wireless network (108). The wirelessnetwork (108) is at least one of a 3G, 4G LTE, 4G WiMAX, WiFi,Bluetooth, SuperWiFi, and another wireless standard.

The processing module (110) comprising a speech recognition module(114), an activity recognition module (116), and a classifier module(118). The processing module (110) is configured to receive the speechdata from the at least one audio sensor (102) and determine a firstcategory information and a time associated with the first categoryinformation in response to the computation of the speech data associatedwith the child by using a speech recognition machine learning module.The first category information is at least one of a crying, sad, happy,angry, tired, frustrated, bored, delighted, extremely delighted,discomfort, positive, neutral, negative, very disappointed, hungry,frustrated, confused, overstimulation, development crying, thirstcrying, teething crying, and sleepiness. The time can be morning,afternoon, evening, and night. The activity recognition module (116) isconfigured to receive the activity data from the at least one videosensor (104) and determine a second category information in response tothe computation of the activity data by using an activity recognitionmachine learning module. The second category information is at least oneof a but not limited to running, playing, eating, crawling, lying down,standing up, and sleeping.

The classifier module (118) is configured to receive the first categoryinformation and the time associated with the first category informationfrom the speech recognition module (114) and receive the second categoryinformation from the activity recognition module (116), and one or morechild parameters from the input module (106). The classifier module(118) comprises a suggestion machine learning module. The classifiermodule (118) is configured to generate a first suggestion in response tothe presence of at least one or more suggestions associated with thefirst category information, the time associated with the first categoryinformation, the second category information, and one or more childparameters in a suggestion database (120) using the suggestion machinelearning module. If the suggestion database (120) does not have anysuggestion related to the first category information, the timeassociated with the first category information, the second categoryinformation, and one or more child parameters then the classifier module(118) is configured to generate a second suggestion in response to thepresence of at least one or more suggestions associated with the firstcategory information, the time associated with the first categoryinformation, the second category information, and one or more childparameters in a predefined child development database (122). Thepredefined child development database (122) comprises suggestionsrelated to a growing and developing child. For example, the child may becrying due to teething problems, disturbed sleeping, etc. Further, thedisplay module (112) is configured to display the first suggestion, orthe second suggestion received from the classifier module (118). Thedisplay module (112) can be a user interface of application softwarerunning on an electronic device. The electronic device is at least oneof a mobile phone, smart phone, laptop, tablet, PDA, desktop, computer,or any other operating system enabled device.

The display module (112) is configured to prompt the parent or caretakerto provide an input in response to the suggestion displayed by thedisplay module (112). The input can be YES, the suggestion is helpful.NO the suggestion is not helpful, or the parent is enabled to provide asuggestion that is helpful for the child to calm down. The system (100)is configured to update the suggestion database (120) in response to thereceived input from the parent or the caretaker of the child.

The suggestion can be the child is hungry needs milk, the diaper of thechild is wet change the diaper, the child has a fever needs seeing adoctor in time, the child is feeling cold wrap the child with warmclothes, the child gets hurt while running calms the child, the child issleepy let him her sleep, etc.

In one exemplary embodiment, the input module (106) and display module(112) can be the user interface of the software application running onthe electronic device. The parent is enabled to create the child profileby providing one or more child parameters (name, age, height, weight,gender). The child profile is stored in the predefined child developmentdatabase (122) and the suggestion database (120).

In yet another exemplary embodiment, the processing module includes oneor more processors coupled to a memory. The processor is configured toreceive instructions and data from a read-only memory or a random-accessmemory or both.

In one exemplary embodiment, the speech data is at least one of anacoustic data (for example energy and pitch), lexical data, andlinguistic data. The speech recognition machine learning module isconfigured to determine a first category information and a timeassociated with the first category information in response to thecomputation of the speech data. The speech data comprises signalsfeatures related to both verbal and non-verbal cues of the child'sbehavior. By way of example, the signal features may include acoustic,lexical, or discourse information about the signals. Vocal expressionsby the child may include words, utterances, hesitations, and otherdisfluencies, by way of example.

The speech recognition machine learning module is configured to deriveone or more signal features from the received speech data. The speechrecognition machine learning module is configured to perform emotionalrecognition decisions by implementing mapping between the signalfeatures and elements of an emotional ontology.

The emotional ontology provides a gradient representation of the humanexpressive behavior, i.e., provides much greater flexibility andgradation in symbolic descriptions of human expressive behavior.Typically, user-level description of expressions of emotion (for examplelinguistic, cognitive, or affective expressions of emotion) has beenprovided in terms of words or phrases. These words may include thefollowing, by way of example: happy, sad, frustrated, bored, verydisappointing, extremely delighted. Many other descriptors may be used.These descriptors have often been the target of automaticclassification, in conventional emotional recognition systems.

The emotional ontology or expressive language ontology provides a methodof organizing the expressive linguistic space in a way that allows for aflexible and graded representation of the child's emotional states. Theemotional ontology is derived from linguistic descriptions such as wordsand phrases, with or without enriched annotations such as those madeavailable from lexica and other lexical resources. The elements of theemotional ontology may thus include but are not limited to: expressivevariables, clusters of expressive variables, and relations between theclusters and/or the variables.

FIG. 2 illustrates an example flow chart (200) of the child monitoringusing machine learning, in accordance with an exemplary embodiment ofthe present invention. In step (202), input data (204), which comprisesspeech data, activity data, an optional movement data, child parametersare received. In step (206), one or more data processing steps areapplied (applying machine learning to generate suggestions). In step(208), determine a first category information and a time associated withthe first category information from the received speech data using aspeech recognition machine learning module. In one embodiment, thespeech recognition machine learning module has been trained on firstcategory training data, wherein the first category training datacomprises speech data for one or more sample child, one or more acousticdata, lexical data, and linguistic data extracted from the speech datafor the one or more sample infants and one or more category informationrelated to the one or more acoustic data (pitch, length, volume, tone,energy, variation, intensity, clustering, and other audio features),lexical data, and linguistic data.

Again, in step (208), determine a second category information using anactivity recognition machine learning module. The activity recognitionmachine learning module has been trained on activity data for one ormore sample child, wherein the activity data comprises the video datarelated to running, playing, eating, crawling, lying down, sleeping, andstanding up, or any other activity patterns of the child.

Again, in step (208), determine a third category information using themovement machine learning module. The movement machine learning modulehas been trained on movement data for the one or more sample child,wherein the movement data comprises of child body movement, i.e., legs,hands, face, feet, any other movement, or gestures of the child.

Again, in step (208), the classifier module receives the first categoryinformation, and the time associated with the first categoryinformation, the second category information, the third categoryinformation, and the child parameters to generate a suggestion using asuggestion machine learning module. The suggestion machine learningmodule has been trained to analyze the first category information, thesecond category information, the third category information, and thechild parameter with a suggestion database or a predefined suggestiondatabase to generate a first suggestion or a second suggestion.

In step (210), a suggestion is an output to a parent or a caretaker,shown as output data (212). The output data (212) includes a firstsuggestion or a second suggestion to the parent or caretaker of thechild. The output data (212) also prompts the parent to provide an inputwhether the suggestion is helpful or not. Further, based on the parentinput, the suggestion database is updated.

FIG. 3 shows a user interface of the electronic device for providingsuggestions and receiving feedback, and the suggestion database, inaccordance with an exemplary embodiment of the present invention. Thedisplay module (112) i.e., the user interface of the softwareapplication running on the electronic device is enabled to provide afirst suggestion from the suggestion database (120) or a secondsuggestion from the predefined suggestion database. The display module(112) displays “feed milk to the child as the child is hungry”. Theparent of the child acts accordingly to the displayed suggestion.Further, the parent is enabled to provide an input (Yes or No) for aquestion “Is this suggestion helpful”, or the parent is enabled toprovide a star rating, or the parent is enabled to type his hersuggestion which is effective in calming the child or reducing the childdistress. The display module (112) is enabled to transmit the input tothe suggestion database (120) through a network (108). The suggestiondatabase (120) is configured to update the suggestions accordingly tothe received input from the parent.

FIG. 4 illustrates a flow chart of a child monitoring method (400), inaccordance with an exemplary embodiment of the present invention. Atstep (402), capturing a speech data associated with a child using anaudio sensor, capturing an activity data associated with the child usinga video sensor. At step (404), receiving one or more child parameters.At step (406), determining a first category information and a timeassociated with the first category information in response to thecomputation of the speech data, received by a speech recognition moduleof a processing module, using a speech recognition machine learningmodule. At step (408), determining a second category information inresponse to the computation of the activity data, received by anactivity recognition module of the processing module, using an activityrecognition machine learning module. At step (410), receiving, by aclassifier module of the processing module, the first categoryinformation and the time associated with the first category informationfrom the speech recognition module, the second category information fromthe activity recognition module, and one or more child parameters. Theclassifier module comprises a suggestion machine learning module and isconfigured for, at step (412), generating a first suggestion in responseto the presence of at least one or more suggestions associated with thefirst category information, the time associated with the first categoryinformation, the second category information, and one or more childparameters in a suggestion database, or generating a second suggestionin response to the presence of at least one or more suggestionsassociated with the first category information, the time associated withthe first category information, the second category information, and oneor more child parameters in a predefined child development database. Atstep (414), displaying, by a display module, the first suggestion, orthe second suggestion received from the classifier module. At step(416), updating the suggestion database in response to receiving aninput, generated from the interaction with the first suggestion, or thesecond suggestion displayed on the display module, from the displaymodule.

In one exemplary embodiment, the one or more instances of the machinelearning modules used in the present embodiments may, for example,utilize hundreds, thousands, tens of thousands, or more artificialneural nodes. Application of one or more artificial neural networks as acomponent and/or implementation of a component of one or more of thepresent embodiments may therefore permit the continuous improvement in acorrelation of the child and/or infant behavior, which may increase thelikelihood of identification of a cause or contributor to an issue,problem, and or stress.

In yet another exemplary embodiment, machine learning modules mayoperate by receiving input from one or more sensors and determining achild's behavior, emotion, or current state by comparing the receivedinput with prior received input and human behaviors, emotions, or statesof the child.

Many modifications and other embodiments of the disclosure set forthherein will come to mind to one skilled in the art to which theseembodiments pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the embodiments are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Although specific terms are employed hereto, they are used in a genericand descriptive sense only and not for purposes of limitation.

Although the present invention has been described with reference tospecific exemplary embodiments, it will be evident that the variousmodification and changes can be made to these embodiments withoutdeparting from the broader scope of the invention. Accordingly, thespecification and drawings are to be regarded in an illustrative senserather than in a restrictive sense. It will also be apparent to theskilled artisan that the embodiments described above are specificexamples of a single broader invention which may have greater scope thanany of the singular descriptions taught. There may be many alterationsmade in the descriptions without departing from the scope of the presentinvention.

What is claimed is:
 1. A child monitoring system, comprising: at leastone audio sensor, located at a first location, configured to capture aspeech data associated with a child; at least one video sensor, locatedat a second location, configured to capture an activity data associatedwith the child; an input module, configured to receive one or more childparameters, wherein the one or more child parameters comprise age,gender and at least one of height and weight; a processing modulecomprising: a speech recognition module configured to receive the speechdata from the at least one audio sensor, and determine a first categoryinformation and a time associated with the first category information inresponse to computation of the speech data associated with the child byusing a speech recognition machine learning module; an activityrecognition module configured to receive the activity data from the atleast one video sensor, and determine a second category information inresponse to computation of the activity data by using an activityrecognition machine learning module; a classifier module configured toreceive the first category information and the time associated with thefirst category information from the speech recognition module, andreceive the second category information from the activity recognitionmodule, and one or more child parameters from the input module; whereinthe classifier module comprises a suggestion machine learning module,the classifier module being configured to: generate a first suggestionin response to a presence of at least one or more suggestions associatedwith the first category information, the time associated with the firstcategory information, the second category information, and one or morechild parameters in a suggestion database, and when the suggestiondatabase does not contain the at least one or more suggestionsassociated with the first category information, the time associated withthe first category information, the second category information, and theone or more child parameters for generating the first suggestion, theclassifier module is further configured to generate a second suggestionin response to a presence of at least one or more suggestions associatedwith the first category information, the time associated with the firstcategory information, the second category information, and one or morechild parameters in a predefined child development database; and adisplay module configured to display the first suggestion or the secondsuggestion received from the classifier module; wherein the suggestiondatabase is configured to be updated in response to receiving an inputfrom the display module generated from the interaction with the firstsuggestion or the second suggestion displayed on the display module. 2.The system of claim 1, wherein the first category information is atleast one of crying, sad, happy, angry, tired, frustrated, bored,delighted, extremely delighted, discomfort, positive, neutral, negative,very disappointed, hungry, frustrated, confused, overstimulation,development crying, thirst crying, teething crying, and sleepiness. 3.The system of claim 1, wherein the time is at least one of morning,afternoon, evening, and night.
 4. The system of claim 1, wherein thesecond category information is at least one of running, playing, eating,crawling, lying down, standing up, and sleeping.
 5. The system of claim1, wherein the predefined child development database comprisessuggestions related to a growing and developing child.
 6. The system ofclaim 1, wherein the speech data is at least one of acoustic data,lexical data, and linguistic data.
 7. A child monitoring method,comprising: capturing speech data associated with a child using an audiosensor; capturing activity data associated with the child using a videosensor; receiving one or more child parameters, wherein the one or morechild parameters comprise age, gender and at least one of height andweight; determining a first category information and a time associatedwith the first category information in response to computation of thespeech data, the speech data being received by a speech recognitionmodule of a processing module, the speech recognition module using aspeech recognition machine learning module; determining a secondcategory information in response to computation of the activity data,the activity data being received by an activity recognition module ofthe processing module, the activity recognition module using an activityrecognition machine learning module; receiving, by a classifier moduleof the processing module, the first category information and the timeassociated with the first category information from the speechrecognition module, the second category information from the activityrecognition module, and one or more child parameters; wherein theclassifier module comprises a suggestion machine learning module, theclassifier module being configured for: generating a first suggestion inresponse to a presence of at least one or more suggestions associatedwith the first category information, the time associated with the firstcategory information, the second category information, and one or morechild parameters in a suggestion database, and when the suggestiondatabase does not contain the at least one or more suggestionsassociated with the first category information, the time associated withthe first category information, the second category information, and theone or more child parameters for generating the first suggestion, theclassifier module is further configured to generate a second suggestionin response to a presence of at least one or more suggestions associatedwith the first category information, the time associated with the firstcategory information, the second category information, and one or morechild parameters in a predefined child development database; displaying,by a display module, the first suggestion or the second suggestionreceived from the classifier module; and updating the suggestiondatabase in response to receiving an input from the display modulegenerated from the interaction with the first suggestion or the secondsuggestion displayed on the display module.
 8. The method of claim 7,wherein the first category information is at least one of crying, sad,happy, angry, tried, frustrated, bored, delighted, extremely delighted,discomfort, positive, neutral, negative, very disappointed, hungry,frustrated, confused, overstimulation, development crying, thirstcrying, teething crying, and sleepiness.
 9. The method of claim 7,wherein the time is at least one of morning, afternoon, evening, andnight.
 10. The method of claim 7, wherein the second categoryinformation is at least one of running, playing, eating, crawling, lyingdown, standing up, and sleeping.
 11. The method of claim 7, wherein thepredefined child development database comprises suggestions related to agrowing and developing child.
 12. The method of claim 7, wherein thespeech data is at least one of acoustic data, lexical data, andlinguistic data.